Q-omics provides the consensus-scored AGFG2 profile across patient tissues and cancer cell-line models. AGFG2 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, AGFG2 is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, AGFG2 protein abundance shows 30,245 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight MESO, HNSC, and LUAD as cancer lineages where AGFG2 shows reproducible signals across survival, tumor–normal expression, and patient cross-omics analyses.
Every result is evaluated using two consensus scores. Sampling consensus measures how consistently a finding is reproduced within a cancer lineage across different conditions. Lineage consensus measures how broadly the result is shared across cancer types, distinguishing pan-cancer signals from lineage-specific patterns.
Premium analyses for AGFG2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AGFG2 survival associations across molecular data types. AGFG2 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (3) and mass-spec protein abundance (9). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AGFG2 RNA expression–survival associations across cancer types. High AGFG2 expression shows unfavorable associations in UCS, but favorable associations in MESO, SKCM, KIRC, ACC and THCA. The MESO Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify MESO as the clearest survival context for AGFG2 RNA expression.
This table summarizes AGFG2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 10. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for AGFG2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AGFG2 shows lower tumor expression in HNSC, COAD, KIRC, KICH and UCEC and higher tumor expression in LIHC. The HNSC box plot shows higher AGFG2 RNA expression in normal versus tumor tissue (log2 FC = −1.472, t-test p < 0.001).
This table shows molecular features associated with AGFG2 in patient tissues and cancer cell lines. In patient samples, AGFG2 shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, AGFG2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OESOPHAGUS, while CRISPR and shRNA rows add functional-dependency signals in SKIN and CNS.
